A Statistical Learning Framework for Phylogenetic Inference: Information, Uncertainty, and Geometry

系统发育推断的统计学习框架：信息、不确定性和几何

• 批准号: 1951474
• 负责人: Vu Dinh
• 金额: $ 21.96万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951474&HistoricalAwards=false
• 关键词: Statistical; Learning; Framework; Phylogenetic; Inference

Phylogenetics, the study of the evolutionary relationships among individuals or groups of organisms from molecular sequence data, is a dominant theme in biological research. In the last few decades, the explosion in the amount of available data for phylogenetic inference has offered great opportunities to further our understanding of various biological processes. At the same time, they also move phylogenetics to a new learning regime, where traditional theories can no longer guide developments and interpretations of phylogenetic algorithms. The main goals of this research are to improve our understanding of the central concepts of phylogenetics through the viewpoints of statistical learning and information theory and to provide essential tools for phylogenetic analyses in this new learning setting. By providing a framework to design, analyze and improve phylogenetic estimators, the research will greatly extend the set of problems for which reliable analyses can be obtained. Most notably, our research in the setting when the number of species increases is especially amenable to the biology of small evolving units, including studies of viruses and antibody-making B-cells. The education component of this study involves mentoring undergraduate and graduate students with independent research in phylogenetics and will produce various demonstrations, tutorials and statistical packages for phylogenetic inference.The proposed research lays out the foundation for explicit quantification of phylogenetic information and uncertainty, with a focus on the setting where sequence data are continually being generated and analyzed. This approach enables the use of local phylogenetic methods as a means to analyze likelihood-based methods and helps investigate systematic ways to stabilize uncertainty while retaining essential information. Two analytical tools to construct and analyze phylogenetic methods in non-asymptotic settings will be developed: a new class of concentration inequalities for evolutionary-related random variables and a Taylor-like local-to-global expansion of phylogenetic likelihood on the space of phylogenetic trees. These newly derived tools will be used to study several important inference problems, including species tree/supertree reconstruction and parameter estimation in viral phylogenetics and trait evolution. The research activities will provide important insights into the influences of phylogenetic information and uncertainty on the stability of a phylogenetic estimate.This award is co-funded with the Statistics program and the Life Science Venture Fund in DMS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

系统发生学是从分子序列数据研究生物个体或群体之间的进化关系，是生物学研究的主导主题。在过去的几十年里，系统发育推断的可用数据量的爆炸提供了很大的机会，以进一步了解各种生物过程。与此同时，他们还将遗传学转移到一个新的学习机制，传统理论不再能够指导系统发育算法的发展和解释。本研究的主要目标是通过统计学习和信息论的观点来提高我们对遗传学核心概念的理解，并为在这种新的学习环境中进行系统发育分析提供必要的工具。通过提供一个框架来设计，分析和改进系统发育估计，该研究将大大扩展的问题，可以得到可靠的分析。最值得注意的是，在物种数量增加的背景下，我们的研究特别适合于小进化单位的生物学，包括病毒和抗体制造B细胞的研究。本研究的教育部分包括指导本科生和研究生进行系统发育遗传学的独立研究，并将为系统发育推断提供各种演示，教程和统计软件包。拟议的研究为系统发育信息和不确定性的明确量化奠定了基础，重点是序列数据不断生成和分析的环境。这种方法使使用本地系统发育方法作为一种手段来分析基于可能性的方法，并有助于调查系统的方法来稳定的不确定性，同时保留必要的信息。两个分析工具，构建和分析系统发育的方法在非渐近设置将开发：一类新的浓度不平等的进化相关的随机变量和泰勒样的局部到全球的扩展系统发育树的空间上的系统发育的可能性。这些新衍生的工具将被用来研究几个重要的推理问题，包括物种树/超树重建和参数估计在病毒遗传学和性状进化。该研究活动将为系统发育信息和不确定性对系统发育估计稳定性的影响提供重要见解。该奖项由DMS的统计计划和生命科学风险基金共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

When can we reconstruct the ancestral state? A unified theory

什么时候才能重建祖宗的状态？

• DOI: 10.1016/j.tpb.2022.09.001
• 发表时间: 2022
• 期刊: Theoretical Population Biology
• 影响因子: 1.4
• 作者: Ho, Lam Si;Dinh, Vu
• 通讯作者: Dinh, Vu

When can we reconstruct the ancestral state? Beyond Brownian motion

什么时候才能重建祖宗的状态？

• DOI: 10.1007/s00285-023-01922-8
• 发表时间: 2023
• 期刊: Journal of Mathematical Biology
• 影响因子: 1.9
• 作者: Vu, Nhat L.;Nguyen, Thanh P.;Nguyen, Binh T.;Dinh, Vu;Ho, Lam Si
• 通讯作者: Ho, Lam Si

Convergence of maximum likelihood supertree reconstruction

最大似然超树重建的收敛性

• DOI: 10.3934/math.2021513
• 发表时间: 2021
• 期刊: AIMS Mathematics
• 影响因子: 2.2
• 作者: Dinh, Vu;Ho, Lam Si
• 通讯作者: Ho, Lam Si